Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 amplicon distribution (2026.1; renamed to "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier classify-sklearn against a version-matched SILVA 138 or Greengenes2 classifier, and diversity core-metrics-phylogenetic — teaching the .qza/.qzv artifact-and-provenance model and the 2026.1 feature-table summarize change (the former summarize_plus). Use when the request mentions QIIME2, QIIME 2, qiime, 16S, 18S, ITS, amplicon, microbiome, ASV, DADA2 denoising, feature table, taxonomic classification, or core-metrics diversity. For downstream alpha/beta diversity, PCoA, and PERMANOVA on the exported feature table prefer alterlab-scikit-bio; this is conda-only (no pip install). Part of the AlterLab Academic Skills suite.
Instrucciones de origen · Vista previa de solo lectura
name
alterlab-qiime2-amplicon
description
Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 amplicon distribution (2026.1; renamed to "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux quality .qzv), feature-classifier classify-sklearn against a version-matched SILVA 138 or Greengenes2 classifier, and diversity core-metrics-phylogenetic — teaching the .qza/.qzv artifact-and-provenance model and the 2026.1 feature-table summarize change (the former summarize_plus). Use when the request mentions QIIME2, QIIME 2, qiime, 16S, 18S, ITS, amplicon, microbiome, ASV, DADA2 denoising, feature table, taxonomic classification, or core-metrics diversity. For downstream alpha/beta diversity, PCoA, and PERMANOVA on the exported feature table prefer alterlab-scikit-bio; this is conda-only (no pip install). Part of the AlterLab Academic Skills suite.
Requires the QIIME 2 amplicon conda environment (cannot be pip-installed); commands are run via the `qiime` CLI. Pretrained classifiers and reference data are downloaded from the QIIME 2 Library. The helper scripts in scripts/ are stdlib-only and run under `uv run python` without a QIIME 2 env.
The command-line, workflow-runner entry point for marker-gene (amplicon) microbiome
analysis. Given raw demultiplexed paired-end reads, it walks the canonical QIIME 2
order — import → primer trim → denoise → classify → diversity — and teaches the two
things people get wrong most: trimming primers BEFORE DADA2, and the .qza/.qzv
provenance model. It is the raw-data-to-result pipeline that hands a feature table off
to in-memory analysis skills (see routing below).
Pinned to QIIME 2 2026.1 (the amplicon distribution). Forward-compat note: the
distribution is renamed qiime2 in 2026.4 — the env name and channel URL change, the
plugin commands below do not.
When to Use This Skill
Use this skill when the request involves running an amplicon / microbiome pipeline from
sequencing reads:
"Run a QIIME 2 16S pipeline on my paired-end reads."
"I have ITS amplicon FASTQs — denoise with DADA2 and assign taxonomy."
"Build a feature table / ASV table and classify against SILVA."
"Pick truncation lengths from my quality plot and run core-metrics diversity."
"How do I trim primers before DADA2 in QIIME 2?"
"What's the right order of QIIME 2 commands?"
Does NOT Trigger — route these elsewhere
The request is really about…
Route to
Alpha/beta diversity, UniFrac, PCoA ordination, PERMANOVA on an already-exported feature/distance table (in-memory, Python)
alterlab-scikit-bio
Building / manipulating a phylogenetic tree, tree visualization, or comparative phylogenetics outside QIIME 2
Reading/writing BAM/SAM/VCF, alignment file surgery
alterlab-pysam
This skill is amplicon (marker-gene) only. If the data is shotgun metagenomic,
single-cell, or anything other than 16S/18S/ITS marker-gene sequencing, say so and stop.
The Artifact Model (.qza / .qzv) — read this first
Everything in QIIME 2 is a typed, zipped artifact that records its own provenance:
.qza — a QIIME 2 Artifact: data (a feature table, sequences, a classifier)
plus an embedded semantic type (e.g. SampleData[PairedEndSequencesWithQuality],
FeatureTable[Frequency]) and a full provenance graph of every action that
produced it.
.qzv — a Visualization: a human-viewable report (quality plots, summaries,
diversity emperor plots). Drag it into https://view.qiime2.org (offline, in-browser)
or run qiime tools view file.qzv.
Provenance is the reproducibility win: any .qza/.qzv carries the exact commands,
parameters, and plugin versions that made it. Keep artifacts, not just exports.
Treat semantic types as the contract: an action only accepts artifacts of the type it
declares, which is why import (step 1) matters so much.
Primer trimming comes BEFORE DADA2. DADA2 models per-base error rates; leftover
primer/adapter bases corrupt that error model and inflate spurious ASVs. Trim with
cutadapt trim-paired first, then denoise. (If your reads are already primer-free —
e.g. EMP-style — you can skip cutadapt, but verify, don't assume.)
0. Install / activate the environment (conda only — no pip)
QIIME 2 cannot be pip-installed; it ships as a conda environment. For 2026.1
(verified env files live in qiime2/distributions):
# macOS (Apple Silicon / Intel) — 2026.1 amplicon distribution
conda env create \
--name qiime2-amplicon-2026.1 \
--file https://raw.githubusercontent.com/qiime2/distributions/dev/2026.1/amplicon/released/qiime2-amplicon-macos-latest-conda.yml
# Linux: swap the filename for qiime2-amplicon-ubuntu-latest-conda.yml
conda activate qiime2-amplicon-2026.1
qiime info # confirm version + installed plugins
For 2026.4, the official command uses the renamed distribution
(--name rachis-qiime2-2026.4, file rachis-qiime2-*-conda.yml); see the QIIME 2
Library quickstart. Full install detail and the env-file matrix:
references/installation.md.
Bulk DADA2 denoising and classifier training are CPU/RAM heavy. On Cem's M4 Max these
run fine locally — keep them off the API and run them in a conda activated shell.
Use a manifest (a TSV mapping sample IDs → absolute FASTQ paths) so you control
exactly which files map to which sample. Format: PairedEndFastqManifestPhred33V2
(verified in q2-types).
--p-discard-untrimmed drops reads where the primer was not found (usually what you
want for targeted amplicons). Action and flag names verified from the q2-cutadapt
source. Primer choice by region (515F/806R, ITS1F/ITS2, etc.):
references/pipeline_steps.md.
3. Denoise with DADA2 → ASVs + feature table
Open demux-trimmed.qzv, read the interactive quality plot, and pick truncation
lengths where median quality drops (forward and reverse independently). Truncated read
length must still leave enough overlap to merge pairs.
Always inspect denoising-stats.qzv: low merge or chimera-survival rates usually mean
trunc-len was too aggressive (no overlap) or primers were not trimmed.
4. Summarize the feature table — note the 2026.1 change
2026.1 breaking change (verified in the release notes): the old summarize
visualizer was renamed _summarize, and the former summarize_plus pipeline is now
summarize — so today's feature-table summarizeis the enhanced summary (it also
emits feature/sample frequency artifacts). Older tutorials calling summarize_plus must
switch to summarize. Details: references/version_notes.md.
5. Assign taxonomy — VERSION-MATCHED classifier
qiime feature-classifier classify-sklearn \
--i-classifier silva-138-99-nb-classifier.qza \ # MUST match your QIIME 2 version
--i-reads rep-seqs.qza \
--o-classification taxonomy.qza
qiime metadata tabulate --m-input-file taxonomy.qza --o-visualization taxonomy.qzv
A pretrained naive-Bayes classifier is pickled scikit-learn — it only loads under the
QIIME 2 release it was trained on. Download the classifier built for your version
from the QIIME 2 Library (SILVA 138 for 16S/18S, Greengenes2 for 16S, UNITE for ITS).
Version-match traps and the train-your-own path:
references/classifiers.md.
Sampling depth is a rarefaction floor: every sample is subsampled to this many reads,
and samples below it are dropped. Pick it from table.qzv to balance depth against sample
retention — never guess. core-metrics-phylogenetic produces Faith's PD, Shannon,
observed features, Bray-Curtis / Jaccard / weighted+unweighted UniFrac distance matrices,
and Emperor PCoA .qzvs in one shot.
For stats and ordination off the exported table (PERMANOVA, custom PCoA, alpha/beta
metrics in Python), export and hand off to alterlab-scikit-bio — that is the
in-memory companion to this pipeline.
scripts/check_artifact.py reads a .qza/.qzv (it is just a zip) and prints its semantic
type, UUID, and the provenance action list without a QIIME 2 install — handy for
sanity-checking that an artifact is what a downstream step expects.
Self-Check Before Reporting
Did primers get trimmed before DADA2? If --p-discard-untrimmed dropped almost
everything, the primer sequences are likely wrong.
Were trunc-lens chosen from the quality .qzv, and does denoising-stats.qzv show
reasonable merge + non-chimeric retention?
Is the classifier version-matched to the running QIIME 2 release?
Is --p-sampling-depth justified from table.qzv, not guessed?
Did you call feature-table summarize (2026.1 = former summarize_plus), not a removed
action name?